Early detection of Alzheimer’s disease (AD) is crucial for timely intervention and management of this debilitating neurodegenerative disorder. However, it demands further serious attention. State-of-the-art vision transformers for multi-class AD detection techniques cannot handle the uncertainty issue arising between various stages of AD. Moreover, AD identification based on magnetic resonance imaging (MRI) scans is likewise computationally expensive. Further, vision transformers used in AD detection often suffer from a lack of interpretability of results. To address these issues, a new vision transformer, namely Fuzzy Granule-based Interpretable Cognitive Vision Transformer (FGI-CogViT) is developed. It has three parts, namely feature extraction, fuzzy logic-based granulation, and I-CogViT-based classification. Various vision and statistical features are computed over the MRI scan image(s). The statistical features are used to obtain the disease-prone regions in terms of fuzzy granules. In these regions, uncertainty may arise among the different stages of AD. Fuzzy logic-based rules are defined to obtain the crisp granules. Instead of considering the entire image, statistical features corresponding to the crisp granules are added with vision features for classification tasks through the I-CogViT that consists of three modules, namely residual network, traditional vision transformer, and classification network. These characteristics improve the speed and accuracy of FGI-CogViT. It synergizes the robust feature extraction capabilities of vision transformers with cognitive computing principles, aiming to augment the model’s interpretability. The efficacy of the FGI-CogViT has been demonstrated over 6,460 MRI scan images. Results reveal that FGI-CogViT outperforms some state-of-the-art. Furthermore, robustness checking and statistical significance testing support the findings.

阿尔茨海默病 （AD） 的早期发现对于及时干预和管理这种使人衰弱的神经退行性疾病至关重要。然而，它需要进一步的认真关注。用于多类 AD 检测技术的最先进的视觉转换器无法处理 AD 各个阶段之间出现的不确定性问题。此外，基于磁共振成像 （MRI） 扫描的 AD 识别在计算上同样昂贵。此外，AD 检测中使用的视觉转换器通常缺乏结果的可解释性。为了解决这些问题，开发了一种新的视觉转换器，即基于模糊颗粒的可解释认知视觉转换器 （FGI-CogViT）。它有三个部分，分别是特征提取、基于模糊逻辑的颗粒化和基于 I-CogViT 的分类。在 MRI 扫描图像上计算各种视觉和统计特征。统计特征用于获得模糊颗粒的疾病易感区域。在这些地区，AD 的不同阶段之间可能会出现不确定性。定义基于模糊逻辑的规则以获得脆颗粒。通过由残差网络、传统视觉转换器和分类网络三个模块组成的 I-CogViT，而不是考虑整个图像，而是通过与脆片颗粒相对应的统计特征添加用于分类任务的视觉特征。这些特性提高了 FGI-CogViT 的速度和准确性。它将视觉转换器的强大特征提取能力与认知计算原理协同，旨在增强模型的可解释性。FGI-CogViT 的功效已在 6,460 张 MRI 扫描图像中得到证明。 结果显示，FGI-CogViT 的性能优于一些最先进的技术。此外，稳健性检查和统计显着性检验支持这些发现。